Design and reliability analysis methods, systems, media and equipment for large-capacity molten salt heat storage and exchange systems

Abstract

This invention relates to a design and reliability analysis method, system, medium, and equipment for large-capacity molten salt heat storage and exchange systems. The method includes: determining system parameters and boundary conditions; performing thermal-hydraulic design calculations after determining the system parameters and boundary conditions; conducting structural mechanics and thermal stress analysis; evaluating and selecting material properties; predicting the system's remaining lifespan based on a damage accumulation model; quantitatively evaluating the system's reliability using probabilistic statistical methods; and optimizing and verifying the design based on the quantitative reliability evaluation results. This invention significantly improves design accuracy, system reliability, and economy, and significantly shortens the design cycle. It also solves the problem of insufficient traditional material performance evaluation. It is applicable to the design and evaluation of large-capacity molten salt heat storage and exchange systems in fields such as solar thermal power generation, advanced nuclear energy systems, and industrial waste heat utilization.

Description

Technical Field

[0001] This invention relates to the field of molten salt heat exchange technology, and more specifically, to a design and reliability analysis method, system, medium, and equipment for a large-capacity molten salt heat exchange system. Background Technology

[0002] Large-capacity molten salt heat storage systems, as key equipment for large-scale energy storage and efficient heat transfer, play an important role in renewable energy utilization and industrial energy conservation. However, existing technologies suffer from the following problems in design methods and reliability assessment: The design methodology is overly simplistic: traditional molten salt system designs are often based on empirical formulas and simplistic assumptions, failing to fully consider the complex physical phenomena in actual operation. For example, thermal stratification in storage tanks is often ignored, leading to inaccurate temperature field predictions; the multi-field coupling effects of thermo-mechanical-chemical fields are not fully considered; and the calculation of thermal expansion stress in piping systems is overly conservative or inaccurate.

[0003] Reliability assessment lacks a systematic approach: existing methods are mostly qualitative or empirically based quantitative assessments, lacking systematic reliability analysis methods based on physical models. For example, life prediction often uses empirical formulas without considering damage accumulation mechanisms; mathematical models linking component failures to system function have not been established; and probabilistic reliability assessment methods that consider uncertainties are lacking.

[0004] Insufficient material performance evaluation: Inadequate research on the long-term performance degradation mechanism of materials in molten salt environment: The creep-fatigue interaction mechanism is unclear; The corrosion-mechanical property coupling effect is not adequately considered; There is a lack of predictive models for long-term service performance. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a design and reliability analysis method, system, medium and equipment for a large-capacity molten salt heat exchange system, addressing the problems existing in the prior art.

[0006] The technical solution adopted by this invention to solve its technical problem is: a design and reliability analysis method for a large-capacity molten salt heat exchange system, comprising the following steps: Step S100: Determine system parameters and boundary conditions; Step S200: After determining the system parameters and boundary conditions, perform thermal-hydraulic design calculations; Step S300: Perform structural mechanics and thermal stress analysis; Step S400: Evaluate and select material properties; Step S500: Predict the remaining lifespan of the system based on the damage accumulation model; Step S600: Perform a quantitative reliability assessment of the system based on probabilistic statistical methods; Step S700: Optimize and verify the design based on the results of the reliability quantitative assessment.

[0007] In the design and reliability analysis method for a large-capacity molten salt heat storage system described in this invention, step S200 includes: Step S201: Perform thermal stratification analysis and calculation of the storage tank based on the three-dimensional unsteady heat transfer model to obtain the number of thermal strata; Step S202: Perform heat transfer calculations on the heat exchanger based on the heat exchanger heat transfer model to obtain the overall heat transfer coefficient and heat transfer area; Step S203: Calculate the total pressure drop of the pipeline system based on the pipeline pressure drop model; Step S204: Calculate the system heat loss based on the composite insulation structure heat loss model and perform insulation optimization design.

[0008] In the design and reliability analysis method for a large-capacity molten salt heat storage system described in this invention, step S300 includes: Step S301: Based on the thermal results output in step S200, calculate the temperature field distribution of the structural components and determine the thermal load; Step S302: Perform thermo-mechanical coupling analysis and strength assessment using the finite element method; Step S303: Calculate the thermal expansion and design compensation measures; Step S304: Conduct a preliminary fatigue life assessment based on the thermal stress amplitude.

[0009] In the design and reliability analysis method for a large-capacity molten salt heat storage system described in this invention, step S400 includes: Step S401: Test the high-temperature mechanical properties of the material and establish a creep constitutive model; Step S402: Evaluate the corrosion performance of the material in a molten salt environment and establish a corrosion prediction model; Step S403: Analyze the creep-fatigue interaction mechanism and conduct corrosion assessment on the component; Step S404: Establish a multi-objective optimization model for material selection.

[0010] In the design and reliability analysis method for a large-capacity molten salt heat storage system described in this invention, step S500 includes: Step S501: Identify the failure mechanism; Step S502: Define the damage parameters of the failure mechanisms and calculate the damage contribution of each failure mechanism; Step S503: Establish a damage accumulation model and perform multi-mechanism damage accumulation based on the damage contribution of each failure mechanism; Step S504: Predict the remaining lifespan of the system based on the current damage accumulation results.

[0011] In the design and reliability analysis method for a large-capacity molten salt heat storage system described in this invention, step S600 includes: Step S601: Identify the uncertainty parameters and determine their probability distribution; Step S602: Calculate the component reliability using a stress-intensity interference model and perform sensitivity analysis to identify key parameters among the uncertainty parameters; Step S603: Establish a system reliability model and perform a quantitative assessment of the system's reliability to obtain the system reliability. Step S604: Develop a risk-based detection strategy based on the system reliability and the component reliability.

[0012] In the design and reliability analysis method for a large-capacity molten salt heat storage system described in this invention, step S700 includes: Step S701: Based on the reliability quantitative assessment results, perform sensitivity analysis on key parameters to identify key variables; Step S702: Establish a multi-objective optimization model and perform optimization design to obtain an optimal solution; Step S703: Verify the optimization scheme through experiments or numerical simulations; Step S704: Iterate and improve the design based on the verification results until all design requirements are met.

[0013] This invention also provides a design and reliability analysis system for a large-capacity molten salt heat storage system, comprising: The parameter and boundary determination unit is used to determine the system parameters and boundary conditions. The thermal-hydraulic calculation unit is used to perform thermal-hydraulic design calculations after the system parameters and boundary conditions are determined. The structural and thermal stress analysis unit is used for structural mechanics and thermal stress analysis. The performance evaluation and selection unit is used to evaluate and select material properties. The life prediction unit is used to predict the remaining life of the system based on the damage accumulation model. The reliability analysis unit is used to perform quantitative reliability assessment of the system based on probabilistic and statistical methods. The design optimization and verification unit is used to optimize and verify the design based on the results of the reliability quantitative assessment.

[0014] The present invention also provides a storage medium storing a computer program adapted for loading by a processor to execute the steps of the design and reliability analysis method for a large-capacity molten salt heat exchange system as described above.

[0015] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the design and reliability analysis method for a large-capacity molten salt heat exchange system as described above by calling the computer program stored in the memory.

[0016] The design and reliability analysis method, system, medium, and equipment for large-capacity molten salt heat storage and exchange systems of this invention have the following beneficial effects: They include: determining system parameters and boundary conditions; performing thermal-hydraulic design calculations after determining the system parameters and boundary conditions; conducting structural mechanics and thermal stress analysis; evaluating and selecting material properties; predicting the system's remaining lifespan based on a damage accumulation model; quantitatively evaluating the system's reliability based on probabilistic statistical methods; and optimizing and verifying the design based on the quantitative reliability evaluation results. This invention can significantly improve design accuracy, system reliability, and economy, and significantly shorten the design cycle. It also solves the problem of insufficient traditional material performance evaluation. It can be applied to the design and evaluation of large-capacity molten salt heat storage and exchange systems in fields such as solar thermal power generation, advanced nuclear energy systems, and industrial waste heat utilization. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart illustrating the design and reliability analysis method for a large-capacity molten salt heat storage and exchange system provided by the present invention. Figure 2 This is a flowchart of thermal hydraulic design calculation provided by the present invention; Figure 3 This is a flowchart of structural mechanics analysis provided by the present invention; Figure 4 This is a flowchart of the material performance evaluation process provided by the present invention; Figure 5 This is a flowchart of the lifetime prediction process provided by the present invention; Figure 6 This is a reliability analysis flowchart provided by the present invention; Figure 7 This is the optimized iterative flowchart provided by the present invention; Figure 8 This is a logic block diagram of the design and reliability analysis system for the large-capacity molten salt heat storage and exchange system provided by the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To address the problems existing in the design and reliability analysis of traditional large-capacity molten salt heat storage systems, this invention provides a method for the design and reliability analysis of such systems. This method can: establish a complete thermo-hydraulic calculation model to accurately predict system performance; achieve coupled thermo-mechanical-chemical field analysis to ensure structural integrity; establish a quantitative lifetime prediction model based on damage mechanics; construct a system-level probabilistic reliability assessment framework; and achieve economic optimization while ensuring safety and reliability. refer to Figure 1 In a preferred embodiment, the design and reliability analysis method for the large-capacity molten salt heat exchange system includes the following steps: steps S100, S200, S300, S400, S500, S600, and S700. The operations performed in each step are as follows: Step S100: Determine system parameters and boundary conditions.

[0020] Preferably, the system parameters and boundary conditions include: operating parameters, environmental conditions, and performance indicators. Specifically, in this step, the system's operating parameters, environmental conditions, and performance indicators are determined according to design requirements, including but not limited to: thermal storage capacity, operating temperature range, pressure rating, molten salt type and physical properties, environmental conditions, seismic intensity, wind load, design life, and reliability targets.

[0021] Step S200: After determining the system parameters and boundary conditions, perform thermal-hydraulic design calculations.

[0022] In some embodiments, such as Figure 2 As shown, step S200 includes: Step S201: Perform thermal stratification analysis and calculation of the storage tank based on the three-dimensional unsteady heat transfer model to obtain the number of thermal strata.

[0023] The three-dimensional unsteady heat transfer model is a general and fundamental physical and mathematical model framework. It describes the governing equations (continuity equation, momentum equation, energy equation) governing the heat transfer and flow behavior of fluids (molten salt) in a storage tank, and defines the boundary conditions (such as tank wall, liquid surface, and bottom conditions) required to solve these equations. It is a "methodology" or "computational tool".

[0024] The thermal stratification model is a specialized analytical model with clear objectives and application scenarios, established within the framework of the aforementioned three-dimensional unsteady-state heat transfer model, specifically targeting the physical phenomenon of "thermal stratification." Utilizing the three-dimensional unsteady-state heat transfer model as the computational engine, it predicts and analyzes the development, stabilization, and decay processes of temperature stratification within storage tanks through specific initial conditions (initial distribution of cold and hot salt), operating conditions (heat charging / discharging rates), and post-processing of results.

[0025] In this embodiment of the invention, based on a three-dimensional unsteady heat transfer model, the heat charging process is simulated up to the design time to obtain the three-dimensional temperature field distribution inside the tank. Then, the total effective energy change of the molten salt inside the tank is calculated, and the effective energy change under the assumption of complete mixing is calculated. Finally, the number of thermal stratifications is calculated.

[0026] Step S202: Perform heat transfer calculations based on the heat exchanger heat transfer model to obtain the overall heat transfer coefficient and heat transfer area.

[0027] In this embodiment of the invention, the ε-NTU method is used to calculate the heat transfer of the heat exchanger and determine the overall heat transfer coefficient and heat transfer area. Specifically, the ε-NTU method (efficiency-number of heat transfer units method) is a simplified design method based on the first law of thermodynamics in heat exchanger design calculations. It is applicable to the preliminary design and performance verification of heat exchangers when the inlet and outlet temperatures, flow rates, and other operating parameters of the fluid are known or can be estimated. Based on the design temperature drop and flow rate, the required efficiency ε is calculated. Combined with the overall heat transfer coefficient U calculated according to formula (X), the required number of heat transfer units NTU is determined, and then the heat transfer area A is obtained. This method provides basic thermal parameters for subsequent detailed structural design, mechanical analysis, and life assessment. Unlike traditional design, the U-value calculation in this invention fully considers the actual heat transfer characteristics of the molten salt side and the steam side, the influence of fouling, and wall thermal conductivity. It is verified and corrected through subsequent multi-field coupling analysis to ensure the accuracy and reliability of the design results.

[0028] Step S203: Calculate the total pressure drop of the pipeline system based on the pipeline pressure drop model.

[0029] In this embodiment of the invention, the total system pressure drop includes: frictional pressure drop, acceleration pressure drop, gravity pressure drop, and local resistance pressure drop. The mathematical expression for the pipeline pressure drop model is as follows: ; In the formula, Total system voltage drop; For frictional pressure drop; To accelerate the pressure drop; This is due to gravitational pressure drop; This is due to localized resistance pressure drop.

[0030] Step S204: Calculate the system heat loss based on the composite insulation structure heat loss model and perform insulation optimization design.

[0031] In this embodiment of the invention, system heat loss (i.e., total system heat loss) includes heat dissipation from components such as storage tanks, pipelines, and heat exchangers. The mathematical expression for the heat loss model of the composite insulation structure is as follows: ; In the formula, This represents the total heat loss of the system. This is for heat loss from the storage tank; This is for heat loss in the pipeline; Heat loss due to components such as heat exchangers; This is for heat loss from other equipment.

[0032] In this embodiment of the invention, the insulation optimization design is carried out with the goal of minimizing the total life cycle cost.

[0033] pass Figure 2 This invention demonstrates the interrelationships and data transfer relationships among the thermal stratification model, heat exchanger heat transfer calculation model, pipeline pressure drop model, and composite insulation structure heat loss model (i.e., the system heat loss model). Inputs primarily include system parameters, molten salt properties, and geometric parameters. Core models include a three-dimensional unsteady-state tank thermal stratification model, a heat exchanger heat transfer model, a pipeline pressure drop model, and a composite insulation structure heat loss model. Outputs primarily include key design parameters (such as dimensions, area, pressure drop, and heat loss), temperature field, and flow velocity field distribution. This invention is the first to integrate a three-dimensional unsteady-state heat transfer model considering thermal stratification into the system-level design process, improving the accuracy of temperature field prediction.

[0034] Step S300: Perform structural mechanics and thermal stress analysis.

[0035] In some embodiments, such as Figure 3 As shown, step S300 includes: Step S301: Based on the thermal results output in step S200, calculate the temperature field distribution of the structural components and determine the thermal load.

[0036] Specifically, in this step, the thermal results from step S200 are input, the temperature field distribution of the structural components is calculated, and it is converted into a thermal load. The temperature field of the structural components can be obtained by solving the energy equation based on a three-dimensional unsteady-state heat transfer model and then discretizing it using the finite element method. Since the thermal load is caused by the temperature gradient, it can be determined based on the temperature field distribution of the structural components.

[0037] Step S302: Perform thermo-mechanical coupling analysis and strength assessment using the finite element method.

[0038] Specifically, the finite element method is used to perform thermo-mechanical coupling analysis to calculate the stress distribution under combined loads such as temperature, pressure, earthquake, and wind load. In accordance with ASME B31.1, stress linearization and evaluation (i.e., strength evaluation) are performed on key components (such as tank welds, tube sheets, etc.).

[0039] Step S303: Calculate the thermal expansion and design compensation measures.

[0040] Specifically, the thermal expansion displacement (i.e., the amount of thermal expansion) is calculated, so that compensation measures (such as expansion joints) can be designed based on the thermal expansion displacement.

[0041] Step S304: Conduct a preliminary fatigue life assessment based on the thermal stress amplitude.

[0042] In this embodiment of the invention, the thermal stress amplitude refers to the range of stress variation during the thermal cycling process. Its mathematical expression is as follows: ; In the formula, This refers to the thermal stress amplitude. This represents the maximum thermal stress amplitude. This represents the minimum thermal stress amplitude.

[0043] The simplified case for full constraints is as follows: ; In the formula, This refers to the thermal stress amplitude. It is the elastic modulus; The coefficient of thermal expansion of the material; This refers to the range of temperature changes experienced by the component during thermal cycling.

[0044] Actual consideration of constraint coefficients and stress concentration factor : ; A preliminary fatigue life assessment is conducted by calculating the thermal stress amplitude. Figure 3 The document details the closed-loop process of structural integrity analysis, from temperature field calculations to final strength assessment.

[0045] The specific steps are as follows: Step 1: Determine the design cycle: Design life: 30 years; Annual operation: 300 days; Daily cycle: 1 time; Total cycle: Second-rate.

[0046] Step 2: Calculation of thermal stress amplitude: The stress time history of key points was obtained through finite element analysis, and the stress amplitude was extracted. .

[0047] Step 3: SN curve query: Based on the material and operating temperature, refer to the corresponding SN curve in the ASME standard to obtain the allowable number of cycles. .

[0048] Step 4: Damage Calculation Using Miner's linear cumulative damage theory: ; In the formula, It is a cumulative damage factor; This refers to the actual number of cycles a component undergoes at a specific stress level. This refers to the permissible number of cycles a material or component can withstand under the same stress level.

[0049] Step 5: High-temperature correction: For a working temperature of 565°C: Temperature correction for elastic modulus; Considering the creep-fatigue interaction: ; In the formula, Total damage factor; The damage component caused by fatigue mechanisms; This represents the damage component caused by the creep mechanism.

[0050] Example Evaluation Results: Initial Design (304 Stainless Steel): Thermal Stress Range: 797 MPa; Allowable Cycles: 2000; Cumulative Damage: Not satisfied; After optimization (improved geometry + 347H stainless steel): Thermal stress amplitude: 460 MPa; Allowable cycles: 15,000; Fatigue damage: Creep damage: Total damage: The requirements are met.

[0051] Step S400: Evaluate and select material properties.

[0052] In some embodiments, such as Figure 4 As shown, step S400 includes: Step S401: Test the high-temperature mechanical properties of the material and establish a creep constitutive model.

[0053] Specifically, in this step, a high-temperature mechanical property test is first performed, and the specific test method is as follows: Test object selection: For key components of molten salt energy storage systems, 304H, 347H stainless steel and Inconel 617 alloy were selected as representative materials.

[0054] Test contents: short-term tensile property test (20-700°C), creep rupture test (500-700°C, stress range 80-250MPa), low-cycle fatigue test (strain control, Δε=0.4-1.0%).

[0055] Testing standards: Compliance with ASTM E21, ASTM E139, ASTM E606 and other standards.

[0056] Data processing: Obtaining the material's elastic modulus E and yield strength Creep strain Evolution curve over time, fatigue life Key parameters, etc.

[0057] Secondly, a creep constitutive model is established.

[0058] Model selection: An improved Norton-Bailey creep model was adopted, considering the combined effects of temperature, stress, and time. ; In the formula, This represents the steady-state creep strain rate. The creep coefficient is a material-related factor. The applied stress; Stress index; For time; For time index; Creep activation energy; It is the ideal gas constant; This refers to absolute temperature.

[0059] In the formula, A, n, m, and Q are material constants, determined through regression analysis of experimental data. For example, using the least squares method to fit the experimental data, the data for 304H stainless steel in the temperature range of 550-650°C are as follows: ; n = 5.2; m = 0.85; Q = 285 kJ / mol.

[0060] Model validation: Through cross-validation, the model prediction error is less than 15%, which meets the accuracy requirements for engineering applications.

[0061] Step S402: Evaluate the corrosion performance of the material in a molten salt environment and establish a corrosion prediction model.

[0062] In this embodiment of the invention, the specific operation of this step is as follows: (1) Corrosion test method: Test environment: Solar-powered molten salt (60% NaNO3 + 40% KNO3) was used as the corrosive medium, with a temperature range of 400-600°C; Sample preparation: Standard corrosion sample (15×10×3mm), surface polished to Ra<0.4μm; Test protocol: Static immersion test: 500-2000 hours; Dynamic corrosion test: flow rate 0-2 m / s simulates actual flow conditions; Evaluation indicators: corrosion rate (mm / year), corrosion layer thickness, and microstructure analysis.

[0063] (2) Corrosion prediction model: Empirical model: Establishing a temperature-time related corrosion rate prediction formula (i.e., corrosion prediction model): ; In the formula, Corrosion rate; Exposure time; It is the ideal gas constant; This refers to absolute temperature.

[0064] This corrosion prediction model is applicable to predicting the corrosion behavior of 347H stainless steel in solar salt at 400-600°C. For example, the life prediction is as follows: Based on corrosion margin design, the service life of the material in a molten salt environment is calculated: ; In the formula, For the predicted service life; For corrosion allowance; Corrosion rate; The safety factor is set to 2.0.

[0065] Model application: The corrosion depth of 347H stainless steel after 10,000 hours of operation in molten salt at 565°C is predicted to be 0.82 mm.

[0066] Step S403: Analyze the creep-fatigue interaction mechanism and conduct corrosion assessment on the components.

[0067] In this embodiment of the invention, the specific operation of this step is as follows: (1) Interaction experiment study: Experimental design: Strain-controlled fatigue test was adopted, and tensile holding time was introduced into the fatigue cycle.

[0068] Test conditions: Temperature: 550°C; Total strain range: Δε t = 1.0%; Hold time: 0-30 minutes; Waveform: Trapezoidal wave; Test results: Holding time significantly reduced fatigue life; a holding time of 10 minutes reduced the fatigue life of 304H steel from 5200 cycles to 1800 cycles.

[0069] (2) Damage accumulation model: ASME Standard Methodology: The bilinear damage accumulation criterion recommended by ASME BPVC Section III Division 5 is adopted, and its specific model is as follows: ; In the formula, This is fatigue damage; This is the allowable value for fatigue damage (usually determined by the design curve); This is creep damage; The allowable value for creep damage (usually determined by the design curve) is the creep damage value.

[0070] Interaction factor: Define interaction factor I and quantify the acceleration effect of the operation. ; In the formula, It is a creep-fatigue interaction factor; This is fatigue damage; This is creep damage; It represents the total damage value at component failure, measured through actual experiments (under creep-fatigue interaction conditions). It reflects the real interaction effect and is used to calibrate theoretical models.

[0071] For the fatigue test of 304H steel held at 550°C for 10 minutes, I = -0.23, indicating that the damage was accelerated by 23%.

[0072] Design criteria: Safety design criteria established based on test results: Fatigue safety factor: SF f = 2.0; Creep safety factor: SF c = 1.5; Total safety factor: SF total = 3.0.

[0073] (3) Engineering application recommendations: Material selection: <500°C: 304H stainless steel; 500-600°C: 347H stainless steel; 600°C: Inconel 617 alloy. Design optimization: Reduce rapid cycling with large temperature differences; avoid prolonged high-temperature holding; optimize structure to reduce stress concentration. Life management: Establish a remaining life prediction system based on online monitoring, and regularly assess the damage status of critical components.

[0074] Step S404: Establish a multi-objective optimization model for material selection.

[0075] The specific steps are as follows: (1) Establishment of a multi-objective optimization model: 1. Set optimization goals: Establish an optimization model that includes the following core objectives: Objective 1: Minimize total cost (the sum of material, manufacturing, and maintenance costs); Objective 2: Maximize service life (based on the combined life of creep, fatigue, and corrosion); Objective 3: Minimize structural weight.

[0076] 2. Design variable definition: Material type: Selected from the candidate material library (e.g., 304H, 347H, Inconel 617, etc.); Material thickness: Design thickness of key components; Surface treatment process: Key process parameters that affect corrosion resistance.

[0077] 3. Constraints: Strength constraint: σ max ≤ [σ] (allowable stress); life constraint: t life ≥ 30 years (design life); Temperature constraint: T max ≤ T material (Maximum operating temperature of the material).

[0078] (2) Optimization of solution and material selection: 1. Obtaining the Pareto optimal solution: A genetic algorithm is used to obtain the non-dominated solution set: Algorithm: NSGA-II (Non-dominated sorting genetic algorithm); Population size: 50-100; Generations: 100-200; Output: Pareto front (optimal solution set).

[0079] 2. Multi-attribute decision-making method: The TOPSIS method is used to select the final solution from the Pareto solution set: Step 1: Construct the normalized decision matrix; Step 2: Determine the positive and negative ideal solutions; Step 3: Calculate the closeness C. i = D i⁻ / (D i⁺ + D i⁻ Step 4: Press C i Sort the results and select the optimal solution.

[0080] 3. Material selection criteria: Based on the optimization results, the selection criteria are as follows: Economical option: 347H stainless steel (excellent overall performance, moderate cost); Long life option: Inconel 617 alloy (excellent high temperature performance, high cost); Low cost option: 304H stainless steel (suitable for temperatures ≤500°C).

[0081] (3) Application of the example: 1. Molten salt storage tank material selection results: Working conditions: 565°C, 0.5MPa, 30-year service life; Recommended solution: 347H stainless steel, 28mm thickness; Alternative solution: Inconel 617 for local reinforcement in high-temperature areas.

[0082] 2. High-temperature pipeline material selection results: Working conditions: 427.5°C, daily circulation temperature difference of 275°C; Main material: 347H stainless steel; Special parts: elbows and tees use Inconel 617.

[0083] Figure 4 The process demonstrates the complete workflow from basic performance testing, performance modeling, corrosion assessment, and multi-objective optimization. Specifically, step S401 involves acquiring basic data, followed by steps S402 and S403 using the basic data from step S401 to conduct core performance assessments. Finally, step S404 uses the basic model from step S401 and the performance parameters and constraints obtained from the assessments in steps S402 and S403 to construct an optimization model with cost, strength, and corrosion resistance as objective functions, thereby selecting the best materials for different components.

[0084] Step S500: Predict the remaining lifespan of the system based on the damage accumulation model.

[0085] In some embodiments, such as Figure 5 As shown, step S500 includes: Step S501: Identify the failure mechanism.

[0086] Specifically, failure mechanisms include creep, fatigue, corrosion, and thermal shock.

[0087] (1) The failure mechanism adopts a systematic identification method that combines theoretical analysis, numerical simulation and experimental verification, as follows: 1. Identification criteria based on materials science and mechanics theory: Creep: Operating temperature > 0.4Tm (Tm is the melting point). The melting point of 304 stainless steel is 1400°C, 0.4Tm = 560°C, and the actual operating temperature is 565°C, therefore there is a significant risk of creep. Fatigue: Alternating stress amplitude > 0.5σ y (σ) y (This refers to the yield strength). The thermal stress amplitude generated by thermal cycling reaches 400-800 MPa, exceeding 50% of the material's yield strength, thus posing a fatigue risk. Corrosion: The molten salt contains corrosive impurities such as Cl⁻, and the material is in the sensitization temperature range (450-850°C), posing a risk of intergranular corrosion. Thermal shock: Under emergency operating conditions, the temperature change rate is >100°C / h, generating a high temperature gradient and triggering thermal stress shock.

[0088] 2. Thermo-mechanical coupling analysis was performed using finite element software to achieve numerical simulation analysis. Specific calculations included: creep strain accumulation rate, fatigue stress amplitude and cycle number, temperature field distribution and thermal stress, and corrosion thinning rate.

[0089] 3. Experimental verification: Creep: 550-650°C creep endurance test to observe void nucleation and growth; Fatigue: Low-cycle fatigue test (Δε=0.4-1.0%) to record crack initiation life; Corrosion: Molten salt static / dynamic corrosion test to measure corrosion rate; Thermal shock: Rapid heating and cooling test to detect surface cracks.

[0090] (2) Priority ranking of failure mechanisms: Risk assessments were conducted based on failure probability and severity of consequences, and priority was determined as follows: creep > fatigue > corrosion > thermal shock. Creep and fatigue have the highest probability of occurrence and the most severe consequences under molten salt high-temperature cyclic conditions, corrosion is a long-term cumulative effect, and thermal shock only occurs under emergency conditions.

[0091] (3) Dominant failure mechanism: For key components (tanks, pipelines, heat exchangers) of molten salt energy storage systems, creep-fatigue interaction is the dominant failure mechanism. The reason is that prolonged exposure to temperatures above 560°C (significant creep) coupled with daily temperature fluctuations (fatigue accumulation) accelerates material damage through their interaction.

[0092] Step S502: Define the damage parameters of the failure mechanism and calculate the damage contribution of each failure mechanism.

[0093] This invention aims to quantify the contribution of different failure mechanisms to material degradation by defining damage parameters for each failure mechanism and calculating the damage contribution of each mechanism. Its main functions include: transforming the abstract concept of "damage" into a calculable numerical index; distinguishing independent damage from different mechanisms such as creep, fatigue, and corrosion; and providing quantitative basis for life prediction and maintenance decisions. Through damage contribution analysis, dominant failure mechanisms can be identified, thereby enabling targeted optimization of design and development of monitoring and maintenance strategies.

[0094] Specifically, the damage parameter is defined as follows: A unified damage parameter D is defined, with a value ranging from 0 to 1, where 0 represents no damage and 1 represents failure. For different failure mechanisms, parameters with clear physical meanings are defined. Creep damage parameter D c Defined as the ratio of cumulative creep strain to critical creep strain, or the ratio of grain boundary void area fraction to critical void fraction. For example, the critical creep strain of 304 stainless steel at 565°C is approximately 0.15-0.25. Fatigue damage parameter D fBased on the Miner linear accumulation criterion, it is defined as the ratio of the actual number of cycles to the fatigue life at that stress level. For molten salt systems, the stress level corresponding to daily temperature cycles is mainly considered. Corrosion damage parameter D corr Based on the definition of wall thickness reduction, it is the ratio of corrosion thinning to the allowable corrosion margin. The annual corrosion rate of 347H stainless steel in molten salt at 565°C is approximately 0.05-0.1 mm. Thermal shock damage parameter D. ts Defined as the ratio of the actual number of thermal shocks to the critical number of thermal shocks that the material can withstand.

[0095] Damage contribution calculation: First, the damage values ​​for each failure mechanism are calculated independently. Creep damage is calculated using the time fraction method, which is the ratio of actual operating time to creep fracture time at the current stress temperature. Fatigue damage is obtained by the ratio of the actual number of cycles to the fatigue life at the corresponding stress level. Corrosion damage is obtained by multiplying the corrosion rate by the exposure time and then dividing by the allowable corrosion margin. Thermal shock damage is obtained by the ratio of the actual number of thermal shocks to the critical number of shocks. For cases with multiple coupled failure mechanisms, a linear superposition method or a nonlinear model considering interactions can be used. The linear superposition method simply adds the damage values ​​together, and failure is determined when the sum is ≥1. A more realistic approach is to consider the creep-fatigue interaction, introducing an interaction index to reflect the synergistic effect between mechanisms.

[0096] Damage contribution is defined as the proportion of damage value from each failure mechanism to the total damage. For example, after 5 years of operation, a storage tank is calculated to have creep damage of 0.25, fatigue damage of 0.30, corrosion damage of 0.08, and thermal shock damage of 0.02, with a total damage of 0.65. Therefore, the fatigue contribution is 46.2%, creep 38.5%, corrosion 12.3%, and thermal shock 3.0%. Analysis shows that fatigue and creep are the dominant damage mechanisms, accounting for approximately 85% combined.

[0097] Step S503: Establish a damage accumulation model and perform multi-mechanism damage accumulation based on the damage contribution of each failure mechanism.

[0098] In this invention, the damage accumulation model includes either a linear damage accumulation model or a nonlinear damage accumulation model. The core purpose of establishing the damage accumulation model in this invention is to quantitatively describe the evolution of material damage over time during service. Its functions are mainly reflected in three aspects: Predictive function: By extrapolating the model, the remaining lifespan of the material under given operating conditions is predicted, providing a basis for equipment replacement and maintenance plans. Evaluation function: Scientifically evaluating the impact of different operating strategies (such as peak shaving frequency and start-up / shutdown rate) on equipment lifespan, supporting operational optimization decisions. Design function: During the design phase, the model evaluates the lifespan performance of different materials and structural schemes, achieving lifespan-based optimization design. In short, the model links operating parameters (temperature, stress, time) with the material damage state, serving as a mathematical bridge connecting "service conditions" and "lifespan end."

[0099] The linear damage accumulation model is established as follows: The linear model is based on the Miner criterion, and its core assumption is that the damage at each stage is independent and linearly additive. Specifically, it includes: 1. Basic model form: The model expression is: Total damage D = Σ (t i / T i ), where t i T represents the actual service time (or number of cycles) of the material under the i-th operating condition. i This is the time (or number of cycles) required for failure to occur under this operating condition when acting alone. Failure is determined when D ≥ 1.

[0100] 2. Model parameter acquisition: Key parameter T i This needs to be obtained through experiments or databases: Creep damage: T i Creep fracture time t r The values ​​were obtained through high-temperature endurance testing or Larson-Miller parameter extrapolation. Fatigue damage: T i N is the number of fatigue failure cycles. f The values ​​were obtained through strain-controlled or stress-controlled fatigue tests (SN curves) at the same temperature. Corrosion damage: T i The time required for corrosion to penetrate the allowable wall thickness is calculated by obtaining the corrosion rate through a long-term corrosion immersion test.

[0101] 3. Model Establishment: Taking a molten salt storage tank as an example, its daily operating conditions can be simplified into two stages: high-temperature load maintenance (creep-dominated) and cooling process (fatigue-dominated). First, the creep fracture time T of the material at 565°C and working stress is determined experimentally. creep And fatigue life T under specific temperature cycling range fatigue Then, the creep time t experienced by the statistical device was recorded.creep and number of thermal cycles t fatigue Substituting into the formula D = t creep / T creep + t fatigue / T fatigue Calculate cumulative damage.

[0102] The linear damage accumulation model is simple and intuitive, and its parameters are easy to obtain. It is widely used in engineering, especially in scenarios where the mechanisms are relatively independent or the interaction is not significant, or as a preliminary conservative estimate.

[0103] The linear damage accumulation model is established as follows: Nonlinear models are designed to describe the synergistic or offsetting effects between mechanisms such as creep and fatigue, which better reflects actual physical processes. The specific method for establishing them is as follows: 1. Basic Model Framework: Nonlinear models typically employ coupled equations, such as the classic creep-fatigue interaction model. (D c / D c0 )^α + (D f / D f0 )^β = 1; In the formula, D c For creep damage; D f For fatigue damage; D c0 and D f0 α represents the critical damage value under single action; α and β are interaction coefficients, whose values ​​are not equal to 1, reflecting the nonlinear coupling strength.

[0104] 2. Determination of Key Parameters (Interaction Coefficients): Determining α and β is the core of establishing a nonlinear model and must be accomplished through specialized interaction tests. The tests need to be designed as a series of creep-fatigue interaction tests, including pure creep, pure fatigue, and different combinations of holding times and cycle numbers. By performing nonlinear fitting on the test data (failure time or cycle number), the α and β values ​​that best describe the data trend can be derived. For example, for 304 / 347H stainless steel in the 550-600°C range, α and β are typically between 0.7 and 0.9; values ​​less than 1 indicate synergistic damage (mutual aggravation).

[0105] 3. Model Establishment: First, conduct systematic material tests to obtain baseline data for pure creep and pure fatigue (D). c0 D f0 The model was analyzed using a series of interactive experimental data. Next, numerical fitting techniques, such as the least squares method, were used to determine the α and β coefficients in the interaction model (as shown in the equation above). Finally, the model was validated using another set of independent experimental data to examine its predictive accuracy.

[0106] Nonlinear models have clearer physical meanings and can more accurately predict lifespan under complex loads, making them particularly suitable for typical creep-fatigue interaction environments like molten salt systems. They are mainly used for critical components (such as reactor pressure vessels and aero-engine blades) or in high-precision life assessment applications.

[0107] Step S504: Predict the remaining lifespan of the system based on the current damage accumulation results.

[0108] Step S600: Perform a quantitative reliability assessment of the system based on probabilistic statistical methods.

[0109] In some embodiments, such as Figure 6 As shown, step S600 includes: Step S601: Identify the uncertainty parameters and determine their probability distribution.

[0110] This invention identifies uncertain parameters and determines their probability distributions to pinpoint all uncertainties affecting lifespan prediction within a system, and mathematically describes the fluctuation range of these factors. Its fundamental significance lies in acknowledging the imperfections and fluctuations in reality, transforming lifespan from a fixed value into a probability range, thereby enabling a more realistic risk assessment and answering the crucial question: "How certain is this lifespan prediction?"

[0111] The specific identification method is as follows: The sources of uncertainty are mainly fourfold: **Fluctuations in material properties:** This is the most critical uncertainty. Even for materials of the same grade, different production batches and heat treatment processes can lead to performance differences. For example, key parameters such as creep rate, fatigue strength, and corrosion rate of 347H stainless steel exhibit natural fluctuations. We need to identify which material parameters are most sensitive to lifespan and focus on them. **Fluctuations in operating loads:** Actual operating conditions are never completely stable. Molten salt temperatures will fluctuate around the set value, thermal stress will vary depending on support conditions, and the actual number of start-ups and shutdowns may exceed the design value. These fluctuations directly affect the rate of damage accumulation. **Errors in the model itself:** The mathematical models we use to calculate damage are simplifications of complex realities. For example, linear damage accumulation models ignore the mutually reinforcing effects of creep and fatigue, and the mesh coarseness in finite element calculations also affects the accuracy of stress results. Model simplification inevitably introduces errors. **Measurement and monitoring errors:** All input data used for life assessment, such as temperature and pressure readings, are subject to instrument measurement errors. Non-destructive testing methods also have certain uncertainties in measuring crack size.

[0112] Determining the probability distribution (i.e., quantifying statistical properties): After identifying uncertain parameters, a probability distribution needs to be assigned to each parameter to describe its possible range of values ​​and their probability. First, choose an appropriate distribution type. For parameters like material strength and lifespan, which are always positive but can fluctuate significantly, a log-normal distribution is typically used. For parameters formed by the superposition of many small errors, such as measurement errors, a normal distribution is commonly used. If only the maximum and minimum values ​​of a parameter are known, but its tendency is unknown, a uniform distribution is used. Then, determine the specific parameters of the distribution. Ideally, there is a large amount of historical or experimental data, allowing for direct statistical analysis to obtain the mean and standard deviation. For example, collect fatigue test data of the same material from different laboratories and calculate the average lifespan and its dispersion. When data is insufficient, reasonable estimations need to be made based on engineering experience, publicly available standards, or literature. For example, the ASME code recommends values ​​for the fluctuation range of certain high-temperature material properties. Finally, the correlation between parameters needs to be considered. Some uncertain parameters do not fluctuate independently; there is a correlation between them. For example, the creep rate and stress index of a material often fluctuate simultaneously, exhibiting a statistical correlation. Ignoring this correlation would overestimate the overall uncertainty, so it is necessary to use data analysis or physical mechanisms to determine and quantify the degree of correlation.

[0113] In practical applications, each input parameter in the life prediction model needs to be examined one by one to determine whether it is a source of uncertainty. For key parameters, statistical data should be collected or estimated to determine their probability distribution form and parameters. The final output is a clear list of uncertainty parameters that clearly indicates: what are the key uncertainties affecting life; what probability distribution (such as a normal distribution) describes each factor; what is the specific form of this distribution (such as mean and standard deviation); and what are the data sources and basis for this information. This work lays a solid foundation for subsequent probabilistic life prediction and reliability assessment. It enables us to calculate probabilistic conclusions such as "with a 95% confidence level, the component life is no less than 10 years," thereby supporting more scientific and economical maintenance decisions and risk management.

[0114] Step S602: Calculate the component reliability using the stress-intensity interference model and perform sensitivity analysis to identify key parameters among the uncertainty parameters.

[0115] Step S603: Establish a system reliability model and perform a quantitative assessment of the system's reliability to obtain the system reliability.

[0116] Step S604: Develop a risk-based detection strategy based on system reliability and component reliability.

[0117] Specifically, the core of this step is to transform the probabilistic life prediction results into concrete, executable, and cost-effective operation and maintenance plans. Its fundamental value lies in realizing a paradigm shift from "regular preventive maintenance" to "precise predictive maintenance," scientifically quantifying risks, optimizing resource allocation, and significantly reducing the total life cycle cost while ensuring safety.

[0118] The fundamental principle behind the detection strategy is that risk-based maintenance relies on the quantification of two core dimensions: failure probability and failure consequences. Failure probability, derived from probabilistic life prediction models, characterizes the probability of a component failing within a specific time window. Failure consequences are comprehensively assessed from four aspects: safety, environment, production losses, and economic costs. By combining probability and consequences, a risk value can be calculated, allowing for risk ranking of all components and prioritizing high-risk items.

[0119] The specific operation is as follows: Step 1: Risk Matrix Construction and Risk Classification: First, a risk matrix is ​​established. The vertical axis of the matrix represents the failure probability level, usually divided into five levels: "extremely high, high, medium, low, and extremely low," based on the failure probability value given by probabilistic life prediction. The horizontal axis represents the failure consequence level, also divided into five levels, comprehensively evaluated by scoring factors such as downtime, maintenance costs, safety impact, and environmental hazards. Each cell in the matrix corresponds to a risk level (e.g., red represents high risk, yellow represents medium risk, and green represents low risk). All components to be managed will be located in this matrix based on their calculated probability and consequences, completing the risk classification. Step 2: Developing Differentiated Detection Strategies: For components with different risk levels, detection schemes with varying intensity and frequency are developed. For high-risk components, high-frequency, multi-method enhanced detection is adopted. For example, for the molten salt heat exchanger tube bundle, which has the highest risk, online ultrasonic thickness measurement may be performed every six months, endoscopic video inspection may be performed annually, and real-time monitoring may be combined with acoustic emission. For medium-risk components, such as main pipeline welds, routine inspections are performed, possibly with a comprehensive non-destructive test every two years. For low-risk components, such as external support structures, low-intensity monitoring, primarily visual inspection, is used, or the inspection interval is extended. The inspection plan must clearly define the specific methods, locations, acceptance criteria, and execution time. The third step: Plan a multi-tiered maintenance contingency plan: Maintenance contingency plans are directly linked to risk levels and damage status, forming a tiered action guideline. For components found to be damaged but not exceeding limits during inspection, the "monitoring operation" plan is activated, i.e., shortening the inspection cycle and closely monitoring damage development. For components with damage approaching permissible limits, the "planned repair" plan is activated, scheduling repairs during the next planned downtime window. For components with damage exceeding limits or a sudden increase in the probability of failure, the "emergency intervention" plan is immediately triggered, requiring a time-limited shutdown for replacement. The plan must include detailed repair procedures, spare parts requirements, safety measures, and decision-making processes.

[0120] Ultimately, the aforementioned inspection tasks and maintenance plans are integrated into a long-term, rolling maintenance master plan. Using optimization algorithms, while meeting risk control requirements, the inspection and repair windows for multiple components are merged as much as possible to reduce unplanned downtime. Based on spare parts procurement cycles and costs, the inventory levels of critical spare parts are optimized. The ultimate goal is to achieve the best balance between maintenance costs, safety performance, and equipment availability.

[0121] Furthermore, the risk-based detection strategy is not a static document, but a dynamic cycle. Specifically, new data obtained after each inspection (such as actual corrosion amount or newly discovered cracks) must be fed back into the initial damage model and probabilistic prediction model to update and correct the prediction results. This process is called "Bayesian update." Based on the updated risk level, subsequent inspection frequencies and maintenance plans are readjusted. For example, if the inspection results for a component are better than expected and its damage development is slower, the next inspection interval can be appropriately extended; conversely, if unexpected damage is found, its risk level is immediately upgraded and monitoring is strengthened. This allows the maintenance strategy to be continuously optimized and become increasingly accurate as the actual condition of the equipment changes.

[0122] Step S700: Optimize and verify the design based on the results of the reliability quantitative assessment.

[0123] In some embodiments, such as Figure 7 As shown, step S700 includes: Step S701: Based on the reliability quantitative assessment results, conduct sensitivity analysis of key parameters to identify key variables.

[0124] The purpose of this step is to identify the design, material, and operational parameters (i.e., key variables) that have the greatest impact on system lifespan and risk, thereby clarifying the direction of optimization and focusing on these key variables. Specifically: Parameter selection: Select variable parameters that may affect lifespan and risk, mainly including: design parameters, such as the geometric dimensions (wall thickness, transition fillet radius) and structural form of key parts of the component; material parameters, such as considering the use of higher-grade heat-resistant alloys or adjusting the heat treatment process to optimize the microstructure; operating parameters, such as maximum operating temperature, temperature change rate (start-stop rate), and insulation pressure.

[0125] Sensitivity Analysis Method: Based on the established probabilistic life prediction model, the "perturbation analysis method" or "variance-based analysis method" is employed. With other parameters fixed, a single parameter is systematically changed within a reasonable range (e.g., increasing the design wall thickness by 5% or 10%, or decreasing the peak operating temperature by 10°C or 20°C), and its impact on key output indicators (such as median life, failure probability, and risk level) is observed and calculated. Results Output: The sensitivity ranking of each parameter is obtained. For example, the analysis may find that the "maximum operating temperature" and the "corner radius of the stress concentration area" have the highest sensitivity to life, with even small changes leading to significant life improvements; while the "wall thickness in the uniform region," after reaching a certain value, has a very small marginal contribution to life with further increases.

[0126] Step S702: Establish a multi-objective optimization model and perform optimization design to obtain an optimization scheme.

[0127] The purpose of this step is to find the optimal balance between conflicting objectives (such as lifespan, cost, and weight / efficiency) and to provide a quantified optimized design solution (i.e., the optimized solution). Specifically: (1) Model establishment: Design variables: Key variable parameters identified in sensitivity analysis (such as wall thickness t, fillet radius R, operating temperature T) max The objective function is typically set as multiple objectives that need to be optimized simultaneously. For example: Objective 1: Maximize predicted lifetime (or minimize failure probability); Objective 2: Minimize material cost / manufacturing cost / operating energy consumption; Objective 3: Minimize component weight (important to the supporting structure). Constraints include geometrical limitations, process feasibility, safety regulations (such as minimum wall thickness), and operating condition ranges.

[0128] (2) Optimization solution: A multi-objective optimization algorithm (such as the NSGA-II genetic algorithm) is used to solve the problem. This algorithm can explore the entire design space and generate a set of Pareto optimal solutions. "Pareto optimal" means that any solution in the set cannot be further improved without compromising at least one other objective. For example, one solution is "lifetime increased by 50%, cost increased by 20%"; another solution is "lifetime increased by 20%, cost remains unchanged".

[0129] (3) Decision-making and output: Designers select the most suitable final solution from the Pareto optimal set based on engineering priorities (e.g., whether safety or economy is more important). For example, they might choose the solution that "maximizes lifespan while increasing cost by no more than 15%". The recommended values ​​for the specific design parameters corresponding to this solution are then output.

[0130] Step S703: Verify the optimized scheme through experiments or numerical simulations.

[0131] The purpose of this step is to conduct virtual or real-world verification of the optimized new solution to ensure its performance meets expectations and to expose potential problems. Specifically: (1) Selection of verification methods: High-fidelity numerical simulation is the preferred rapid verification method. For example, it allows for more detailed fluid-structure-thermal coupling simulations of optimized new geometric models to verify whether the temperature and stress fields are superior to the original design; or it enables transient dynamic analysis to simulate the response under extreme thermal shock conditions. Scale-down or full-scale experiments are necessary for fundamental design changes or the application of new materials. Scale-down prototypes can be manufactured and subjected to accelerated life or destructive testing in a simulated environment to directly observe failure modes and lifespan.

[0132] (2) Verification content: Focus on the key issues targeted by the optimization. For example, if the optimization is mainly aimed at fatigue life, the verification should focus on whether the stress concentration factor of the new design under cyclic loading is effectively reduced and whether the plastic strain amplitude is reduced.

[0133] Step S704: Iterate and improve the design based on the verification results until all design requirements are met.

[0134] The purpose of this step is to fine-tune or revise the design based on verification feedback, forming a final, reliable design solution and solidifying design knowledge. Specifically: (1) Result evaluation and comparison: Compare the verification results (such as simulated new stress values ​​and experimentally measured life data) with the prediction results of the optimization model. Assess whether the optimization objectives have been achieved and whether there are any unforeseen negative effects.

[0135] (2) Design iteration: If the verification results meet or exceed expectations, the optimized design is confirmed to be effective, and the final design stage can proceed. If the verification reveals deficiencies (such as stress reduction not meeting expectations, or the emergence of new weak points), the causes need to be analyzed, and the process should return to step S701 or step S702. It may be necessary to adjust the parameter range of the sensitivity analysis or modify the constraints in the optimization model, and then conduct a new round of optimization and verification.

[0136] (3) Knowledge consolidation and improvement: The validated optimized design parameters, corresponding performance improvement data, and validation process are then incorporated into the company's design specifications, material selection database, or operation and maintenance guidelines. This completes a closed loop from "problem analysis" to "solution solution" and then to "experience accumulation," continuously improving overall design capabilities.

[0137] This method, for the first time, systematically integrates thermal hydraulics, structural mechanics, materials science, and reliability engineering, establishing a complete theoretical calculation system. Through this method, design accuracy, system reliability, and economy can be significantly improved. It is applicable to the design and evaluation of large-capacity molten salt heat storage and exchange systems in fields such as solar thermal power generation, advanced nuclear energy systems, and industrial waste heat utilization. Using the method of this invention, the following technical effects can be achieved: Design accuracy is significantly improved: temperature field prediction error is reduced from 15-20% in traditional methods to less than 5%; stress calculation accuracy is improved by more than 30%; and life prediction accuracy is improved by more than 50%. Significantly improved reliability: System reliability has increased from 0.90-0.95 in traditional designs to 0.98-0.99; unexpected downtime rate has been reduced by more than 60%; and maintenance costs have been reduced by more than 30%.

[0138] Significantly improved economic efficiency: Under the same safety level, material usage is reduced by 10-15%; system efficiency is increased by 5-8%; and total life cycle cost is reduced by 20-25%.

[0139] Design cycle shortened: Through a systematic approach, the design cycle is shortened by 30-40%; the number of design iterations is reduced by more than 50%; and the cost of design changes is reduced by more than 60%.

[0140] The following examples illustrate this.

[0141] Example 1: Design of a molten salt system for a 50MWe solar thermal power plant: System Design: 1.1 System Parameter Definitions: Design Inputs: Power Plant Capacity: 50 Mwe; Thermal Storage Time: 7 hours; Molten Salt Type: Solar Salt (60% NaNO3 + 40% KNO3); Operating Temperature: Cold Salt 290°C, Hot Salt 565°C; Design Pressure: Atmospheric Pressure (Storage Tank), 2.5 MPa (Heat Exchanger); Design Life: 30 years; Reliability Target: 0.99 (30 years). Performance Indicators: System Efficiency: ≥42%; Heat Loss Rate: ≤2% / day; Temperature Uniformity: Axial Temperature Difference ≤50°C.

[0142] 1.2 Thermal-hydraulic design calculations: Thermal storage tank design: This invention uses a thermal stratification model for calculation, namely, the number of thermal stratifications S. t The following steps are used to calculate: Based on a three-dimensional unsteady-state heat transfer model, the heating process was simulated up to the design time, and the three-dimensional temperature field distribution inside the tank was obtained, defined as follows: T(x,y,z) .

[0143] Calculate the total effective energy change of the molten salt in the storage tank: ; In the formula, The change in the effective energy (T) of the molten salt in the storage tank under actual stratification conditions; Density of molten salt; The isobaric specific heat capacity of molten salt; For ambient reference temperature; For the volume of the storage tank; The effective energy of the molten salt in the tank at the initial moment.

[0144] Calculate the change in effective energy under the assumption of complete mixing: ; In the formula, This represents the change in effective energy assuming the molten salt in the storage tank is completely and uniformly mixed. This refers to the uniform temperature of the molten salt after complete mixing.

[0145] Calculate the number of thermal layers: .

[0146] in, The thermal stratification number is the ambient reference temperature.

[0147] This calculation method can accurately quantify the quality of thermal stratification in storage tanks, providing a quantitative basis for optimizing tank structures.

[0148] Boundary conditions: Tank wall: convection + radiation composite boundary; Free liquid surface: radiation heat transfer dominant; Bottom: adiabatic boundary (considering foundation insulation). Calculation results: Tank dimensions: diameter 25m, height 15m; Number of thermal stratifications: S t = 0.35 (end of heating); heat loss: 230kW (total); insulation layer thickness: tank wall 400mm, top cover 300mm.

[0149] Heat exchanger design: This invention employs the ε-NTU method for the design of shell-and-tube heat exchangers, as detailed below: Heat transfer calculation: The overall heat transfer coefficient is calculated using a heat exchanger heat transfer model. The mathematical expression for the heat exchanger heat transfer model is as follows: ; In the formula, The overall heat transfer coefficient is based on the outer surface area of ​​the tube; is the convective heat transfer coefficient of the shell-side fluid; For shell-side fouling thermal resistance; This refers to the outer diameter of the heat exchange tube. This refers to the inner diameter of the heat exchange tube. The thermal conductivity of the pipe wall material; Thermal resistance due to fouling in the tube side; is the convective heat transfer coefficient of the fluid in the tube.

[0150] Heat transfer coefficient on the molten salt side (turbulent flow): ; In the formula, The Nusselt number is the number on the molten salt side. The Reynolds number on the molten salt side; The Prandtl number is the molten salt side; The dynamic viscosity of the molten salt at the bulk temperature; This represents the dynamic viscosity of the molten salt at the tube wall temperature.

[0151] Steam-side heat transfer coefficient (boiling): ; In the formula, It is a convection boiling enhancement factor; The coefficient of heat transfer is the single-phase convective heat transfer coefficient. It is a nucleus boiling inhibition factor; The heat transfer coefficient for nucleation boiling is given.

[0152] Design results: Superheater: A=4500m², U=450W / (m²·K); Reheater: A=3200m², U=480W / (m²·K); Preheater: A=2800m², U=420W / (m²·K). Where A represents the heat exchanger area.

[0153] Piping system design: The total pressure drop is calculated based on the pipeline pressure drop model, and the specific expression is as follows: ; In the formula, For total pressure drop, For frictional pressure drop; For acceleration voltage drop; This is due to gravitational pressure drop; This is due to localized resistance pressure drop.

[0154] The calculations for each pressure drop are as follows: Frictional pressure drop: ; In the formula, For frictional pressure drop; f The friction coefficient is calculated based on the Reynolds number Re and the relative roughness ε / D. L This refers to the length of the pipe. The hydraulic diameter of the pipe; For fluid density; The average flow velocity of the fluid.

[0155] coefficient of friction f The calculation is as follows: Laminar flow region (Re≤2000): f =64 / Re; the Colebrook-White formula is used in the turbulent region: ; Acceleration pressure drop: ; In the formula, For acceleration voltage drop; , Here are the fluid density and velocity at pipe section 1; , Here are the fluid density and velocity at pipe section 2.

[0156] For pipes with a constant cross-section, the density change caused by temperature variation is the primary consideration: ; In the formula, For acceleration voltage drop; The fluid density is at the reference temperature. is the coefficient of volumetric expansion of the fluid; , Let be the fluid temperature at section 1 and section 2.

[0157] Gravitational pressure drop: ; In the formula, This is due to gravitational pressure drop; For fluid density; It is the acceleration due to gravity; This refers to the height difference between the pipeline inlet and outlet.

[0158] For inclined pipes: ; In the formula, This is due to gravitational pressure drop; For fluid density; It is the acceleration due to gravity; The angle between the pipe and the horizontal plane is L; the length of the pipe is L.

[0159] Local resistance pressure drop: ; In the formula, This represents the local resistance coefficient of each pipe fitting; For fluid density; Let be the fluid velocity at the location of the i-th local resistance component; n is the number of local resistance components.

[0160] Hydraulic calculations: Main pipe diameter: DN500; Branch pipe diameter: DN300; Total pressure drop: 0.35MPa; Pump power: 550kW.

[0161] Heat loss calculation: The total heat loss of the system is calculated using the following formula: ; In the formula, This represents the total heat loss of the system. This is for heat loss from the storage tank; This is for heat loss in the pipeline; Heat loss due to components such as heat exchangers; This is for heat loss from other equipment.

[0162] The heat loss of the storage tank is calculated using the following formula: ; In the formula, This is for heat loss from the storage tank; This refers to the height of the cylindrical section of the storage tank. The temperature of the molten salt; The ambient temperature; The thermal conductivity of the insulation material; This refers to the inner diameter of the storage tank. The thickness of the insulation layer on the side wall of the storage tank; The overall heat transfer coefficient (convection + radiation) between the outer surface of the insulation layer and the environment. The thickness of the insulation layer on the top cover of the storage tank.

[0163] Calculation of heat loss rate: ; In the formula, This refers to the heat loss rate; This represents the total heat loss of the system. This represents the total mass of molten salt within the system. Design operating temperature rise for molten salt; Specific heat capacity.

[0164] Thermal insulation optimization design: The goal is to minimize the total life cycle cost. ; In the formula, Total life-cycle cost; This refers to the initial investment cost of the insulation layer; Let be the energy loss cost due to heat loss in year t; For system design life; is the discount rate.

[0165] Optimization steps: Determine design conditions and constraints; initially select insulation materials and establish a thermophysical model; establish the functional relationship between heat loss and thickness; conduct economic analysis and sensitivity analysis; determine the optimal insulation thickness and structure. In the example, through optimization, the following results were obtained: tank wall insulation thickness: 400mm; tank top cover insulation thickness: 300mm; pipeline insulation thickness: 200mm (composite structure); total heat loss: 230kW; heat loss rate: 0.34% / day, meeting the design requirement of ≤2% / day.

[0166] Temperature field distribution and thermal load calculation: (1) Temperature field calculation: Solving the energy equation based on a three-dimensional unsteady heat transfer model: ; In the formula, For fluid density; Specific heat capacity; T For temperature; For time; For the fluid velocity vector field; This is the gradient operator; k is the thermal conductivity of the material. This is an internal heat source.

[0167] Boundary conditions include: Convection boundary: Radiation boundary: Heat flow boundary: Temperature boundary: Where k is the thermal conductivity of the solid material; The temperature gradient represents the solid surface temperature; h represents the convective heat transfer coefficient; T represents the solid wall temperature. Indicates the mainstream temperature of the fluid; This represents the Stefan-Boltzmann constant; Tsurr represents emissivity; Tsurr represents ambient radiation temperature; q represents the normal heat flux density of the wall, q>0: the solid dissipates heat outward; q<0: the outside heats the solid; q=0 indicates an adiabatic boundary.

[0168] The solution is obtained by discretization using the finite element method, considering the changes in physical properties with temperature: molten salt: ;Stainless steel: .

[0169] (2) Determination of thermal load: Thermal load is caused by temperature gradient, and the calculation principle is as follows: In the formula, These are components of the thermal stress tensor; The elastic modulus of the material; The coefficient of thermal expansion of the material; The Poisson's ratio of the material; The trace of the thermal strain tensor (volume strain); For Kronecker delta function (unit tensor); It is the difference between the local temperature and the reference temperature (usually the installation or stress-free temperature).

[0170] Specific component thermal load calculation: Axial thermal stress in storage tank: ; In the formula, This refers to the axial thermal stress of the storage tank. The elastic modulus of the material; The coefficient of thermal expansion of the material; The average temperature along the height of the tank wall; Let z be the temperature of the tank wall at height z.

[0171] Pipe thermal stress (fixed at both ends): ; In the formula, This refers to the axial thermal stress (compressive stress) of the pipeline. The elastic modulus of the material; The coefficient of thermal expansion of the material; This is the difference between the pipeline's operating temperature and its installation temperature.

[0172] Combined stress (Von Mises criterion): ; In the formula, This is the equivalent stress (Von Mises stress); Principal stress; This is shear stress.

[0173] Example calculations: For a 50MWe system tank: axial temperature difference: 275°C; material: 304 stainless steel. , The calculated thermal stress is 127 MPa (theoretical maximum value); after considering the actual constraint conditions, it is 52 MPa (axial); the combined stress (including internal pressure, earthquake, and wind load) is 185 MPa; through finite element analysis, the calculation error is <5%, which meets the engineering accuracy requirements.

[0174] 1.3 Structural Mechanics and Thermal Stress Analysis: Load combination: Internal pressure: hydrostatic pressure of liquid column; Temperature load: ΔT=275°C; Seismic load: 0.2g; Wind load: basic wind pressure 0.5kN / m².

[0175] Thermal stress calculation: ; In the formula, Thermal stress caused by temperature difference; This represents the elastic modulus of the material at the operating temperature. The coefficient of thermal expansion of the material; The temperature range that the component can withstand (the difference between the operating temperature and the installation or stress-free temperature). Let be the Poisson's ratio of the material.

[0176] Combined stress: ; In the formula, It is the equivalent stress (Von Mises stress), used for strength assessment under multiaxial stress conditions; This refers to the circumferential stress caused by internal pressure. The axial stress is caused by internal pressure and its own weight; This is shear stress.

[0177] Strength assessment: Allowable stress S = 75 MPa (565°C); Assessment: <3S = 225MPa, passed.

[0178] Pipeline stress analysis: Thermal expansion calculation: ; In the formula, This is the thermal expansion of the pipe; The coefficient of thermal expansion of the pipe material; This is the original length of the pipe; This is the difference between the pipeline's operating temperature and its installation temperature.

[0179] Expansion joint design: Quantity: 3; Type: Bellows type; Compensation amount: 80mm each; Stiffness: 1500N / mm.

[0180] 1.4 Material Performance Evaluation and Selection: (1) Material selection: Establish a multi-objective optimization model: In the formula, , , Let be the weighting coefficient, satisfying This reflects the relative importance of cost, creep performance, and corrosion resistance; For material costs; For reference material costs; This refers to the creep strength of the material under design conditions (such as the creep rupture strength after 100,000 hours). The creep strength of the reference material; This represents the corrosion rate of the material under design conditions. The corrosion rate is used as a reference material.

[0181] Optimization results: Storage tank: 304 stainless steel (good economic efficiency); Heat exchanger: Inconel 617 (good high temperature resistance); High temperature pipeline: 316H stainless steel (good overall performance).

[0182] (2) Performance prediction: Creep properties: ; In the formula, This represents the steady-state creep strain rate. The creep coefficient is a material-related factor, and its unit depends on the exponent n; The applied stress; Stress index; Creep activation energy; It is the ideal gas constant; This refers to absolute temperature.

[0183] For Inconel 617: A = 1.2 × 10 -15 n=5.2, Q=350kJ / mol.

[0184] Corrosion properties: ; In the formula, The unit is for corrosion depth or mass loss; the specific unit depends on the model. Corrosion coefficient related to the material-environment system; This represents the apparent activation energy of the corrosion process. It is the ideal gas constant; Absolute temperature; Exposure time; This is a time index.

[0185] In solar salt at 565°C: k = 0.15 mm / year, =85kJ / mol, m=0.5.

[0186] 1.5 Lifetime Prediction: (1) Failure Mechanism Analysis: Main failure mechanisms: creep failure (high temperature components), fatigue failure (cyclic loading), corrosion failure (molten salt environment), and thermal shock failure (rapid temperature change).

[0187] (2) Damage accumulation model: Linear damage accumulation: ; In the formula, D For linear damage accumulation; Let be the number of cycles experienced under the i-th load condition; The fatigue life under the i-th load condition; This is the critical cumulative damage value, usually taken as 1.0; k This represents the number of load conditions.

[0188] Creep-fatigue interaction: ; In the formula, Let be the holding time under the i-th steady-state load; Let be the creep fracture time under the i-th steady-state load; Let j be the number of cycles experienced under the j-th cyclic load. Let be the number of fatigue failure cycles under the j-th cyclic load; The damage limit value to account for interaction is usually less than or equal to 1.0.

[0189] (3) Lifetime prediction calculation: Taking the superheater tube sheet as an example: Input parameters: Material: Inconel 617; Temperature: 540°C; Stress: 120 MPa; Cycles: 1 time / day.

[0190] Creep life: ; In the formula, These are the Larson-Miller parameters; This is a material constant, usually taken as around 20; This refers to the creep rupture time; For temperature.

[0191] Fatigue life: ; In the formula, For strain amplitude; This is the fatigue strength coefficient; The elastic modulus of the material; The fatigue strength index; It is the fatigue ductility coefficient; It is the fatigue ductility index; This represents the number of failure cycles.

[0192] Substituting the parameters, we get: = 22,000 times.

[0193] Interaction damage: 30-year total cycles: 10950; Fatigue damage: D f = 10950 / 22000 = 0.498; Creep damage: D c = (30×365×24) / (1.2×10 5 = 2.19; Total damage: D total = 0.498 + 2.19 = 2.688>1.

[0194] Conclusion: The design does not meet the lifespan requirements and needs to be optimized.

[0195] 1.6 Reliability Analysis: (1) Reliability calculation: Stress-intensity interference model: ; In the formula, Reliability is the probability that the strength is greater than the stress. Let the strength of the component be a random variable. Let S be the stress on the component, a random variable; P(S>s) represents the probability that the component strength S is greater than the stress s. Let S be the probability density function of intensity S; Let be the probability density function of stress s; the upper limit x of the inner integral represents the upper limit of the strength of the component.

[0196] Normal distribution: ; In the formula, The cumulative distribution function of the standard normal distribution; The average intensity; This represents the average stress value. Strength standard deviation; This represents the standard deviation of stress.

[0197] Calculation results: Storage tank: R=0.9995; Heat exchanger: R=0.998; Pipeline: R=0.9998; Valve: R=0.999; Pump: R=0.997.

[0198] System reliability: .

[0199] (2) Fault tree analysis: Top event: System leakage. Basic event probabilities: Material defects: 0.001; Manufacturing defects: 0.005; Overload operation: 0.01; Excessive corrosion: 0.02; Sealing aging: 0.05.

[0200] Top event probability: .

[0201] 1.7 Design Optimization: (1) Optimization model: Objective function: ; In the formula, For vector functions Perform minimization optimization; Represents the transpose of a vector; It is the cost function; For failure risk function; To maintain the cost function.

[0202] Constraints: Strength constraint: σ ≤ [σ]; Life constraint: L ≥ L d Reliability constraint: R ≥ R d ; Dimensional constraints: Geometric limitations. Where σ is the calculated working stress; [σ] is the allowable stress of the material; L is the predicted component or system life; L d R represents the design required lifespan; R is the calculated reliability; R d This represents the minimum reliability required by the design.

[0203] (2) Optimization results: Through optimization of the superheater tube sheet: material upgrade: Inconel 617 → Inconel 740H; thickness increase: from 50mm to 60mm; stress reduction: from 120MPa to 95MPa.

[0204] Optimized lifespan: creep life increased from 13.7 years to 25.1 years; fatigue life increased from 22,000 cycles to 35,000 cycles; total damage decreased from 2.688 to 0.87; meeting the 30-year design life requirement.

[0205] 1.8 Verification and Confirmation: (1) Numerical verification: Verification calculations were performed using finite element software: Temperature field: error <3% compared to design calculation; Stress field: error <5% compared to design calculation; Life prediction: error <10% compared to detailed analysis.

[0206] (2) Experimental verification: Key design features were validated through scaled-down experiments: thermal stratification experiment: verifying temperature distribution; heat transfer experiment: verifying heat transfer performance; corrosion experiment: verifying material properties; fatigue experiment: verifying life prediction. Verification results: All verification items had errors within the allowable range; the design method was validated; and the design is ready for engineering application.

[0207] Example 2: Molten salt intermediate heat exchanger for a 100MWe advanced nuclear energy system.

[0208] (1) Special design considerations: Design condition differences: Operating temperature: 700°C (higher); Operating pressure: 15MPa (higher); Medium: Chloride salts (more corrosive); Irradiation environment: Irradiation damage needs to be considered.

[0209] (2) Key technology improvements: Material selection: Using the multi-objective optimization method proposed in this invention, the following materials were selected: main material: Haynes 282; special parts: Inconel 718 (high-strength parts); anti-corrosion coating: AL2O3 ceramic coating.

[0210] (3) Structural design: Compact Printed Circuit Board Heat Exchanger (PCHE): Channel size: 2mm × 2mm; Board thickness: 1.5mm; Material: Haynes 282.

[0211] Heat transfer calculation: In the formula, The Nusselt number is dimensionless and characterizes the strength of convective heat transfer. is the Reynolds number, which is dimensionless and characterizes the flow state; Prandtl number, dimensionless, characterizes the ratio of a fluid's momentum diffusivity to its thermal diffusivity; The hydraulic diameter of the channel; The length of the channel is denoted as .

[0212] Lifetime prediction improvements: Lifetime model considering irradiation damage: In the formula, The total damage factor is dimensionless. The damage component caused by the creep mechanism; The damage component caused by fatigue mechanisms; This refers to the damage component caused by irradiation damage (such as atomic dislocation and helium embrittlement).

[0213] Radiation damage: In the formula, The damage component caused by irradiation damage; Neutron fluence rate; The damaged section; t Indicates time.

[0214] (3) Design results: Main parameters: Heat exchange area: 850m²; Heat transfer coefficient: 1200W / (m²·K); Pressure drop: 0.25MPa (both sides); Design life: 40 years; Reliability: 0.995.

[0215] Verification results: Passed ASME standard assessment; meets nuclear safety requirements; already applied in demonstration projects.

[0216] refer to Figure 8 The present invention also provides a design and reliability analysis system for a large-capacity molten salt heat exchange system.

[0217] like Figure 8 As shown, the design and reliability analysis system of this large-capacity molten salt heat exchange system includes: The parameter and boundary determination unit 801 is used to determine system parameters and boundary conditions; The thermal-hydraulic calculation unit 802 is used to perform thermal-hydraulic design calculations after the system parameters and boundary conditions are determined. Structural and thermal stress analysis unit 803 is used for structural mechanics and thermal stress analysis; The performance evaluation and selection unit 804 is used to evaluate and select material properties; The life prediction unit 805 is used to predict the remaining life of the system based on the damage accumulation model. The reliability analysis unit 806 is used to perform quantitative reliability assessment of the system based on probabilistic statistical methods. Design optimization and verification unit 807 is used to optimize and verify the design based on the results of reliability quantitative assessment.

[0218] Specifically, the design and reliability analysis of the large-capacity molten salt heat exchange system and the specific operational procedures between the various units in the system can be referred to the above-mentioned design and reliability analysis method for large-capacity molten salt heat exchange systems, which will not be repeated here.

[0219] Furthermore, an electronic device of the present invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the design and reliability analysis method for a large-capacity molten salt heat exchange system as described above. Specifically, according to embodiments of the present invention, the processes described above with reference to the flowchart can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, when the computer program is downloaded, installed, and executed by an electronic device, it performs the functions defined in the methods of the embodiments of the present invention. The electronic device of the present invention can be a terminal such as a laptop, desktop computer, tablet computer, or smartphone, or it can be a server.

[0220] Furthermore, one type of storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the design and reliability analysis method for a large-capacity molten salt heat exchange system as described above. Specifically, it should be noted that the storage medium described above in the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or apparatus. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0221] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0222] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0223] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0224] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0225] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They do not limit the scope of protection of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A design and reliability analysis method for a large-capacity molten salt heat storage and exchange system, characterized in that, Includes the following steps: Step S100: Determine system parameters and boundary conditions; Step S200: After determining the system parameters and boundary conditions, perform thermal-hydraulic design calculations; Step S300: Perform structural mechanics and thermal stress analysis; Step S400: Evaluate and select material properties; Step S5 00: Predicting the remaining lifespan of the system based on a damage accumulation model; Step S600: Perform a quantitative reliability assessment of the system based on probabilistic statistical methods; Step S700: Optimize and verify the design based on the results of the reliability quantitative assessment.

2. The design and reliability analysis method for a large-capacity molten salt heat storage and exchange system according to claim 1, characterized in that, Step S200 includes: Step S201: Perform thermal stratification analysis and calculation of the storage tank based on the three-dimensional unsteady heat transfer model to obtain the number of thermal strata; Step S202: Perform heat transfer calculations on the heat exchanger based on the heat exchanger heat transfer model to obtain the overall heat transfer coefficient and heat transfer area; Step S203: Calculate the total pressure drop of the pipeline system based on the pipeline pressure drop model; Step S204: Calculate the system heat loss based on the composite insulation structure heat loss model and perform insulation optimization design.

3. The design and reliability analysis method for a large-capacity molten salt heat storage and exchange system according to claim 1, characterized in that, Step S300 includes: Step S301: Based on the thermal results output in step S200, calculate the temperature field distribution of the structural components and determine the thermal load; Step S302: Perform thermo-mechanical coupling analysis and strength assessment using the finite element method; Step S303: Calculate the thermal expansion and design compensation measures; Step S304: Conduct a preliminary fatigue life assessment based on the thermal stress amplitude.

4. The design and reliability analysis method for a large-capacity molten salt heat storage and exchange system according to claim 1, characterized in that, Step S400 includes: Step S401: Test the high-temperature mechanical properties of the material and establish a creep constitutive model; Step S402: Evaluate the corrosion performance of the material in a molten salt environment and establish a corrosion prediction model; Step S403: Analyze the creep-fatigue interaction mechanism and conduct corrosion assessment on the component; Step S404: Establish a multi-objective optimization model for material selection.

5. The design and reliability analysis method for a large-capacity molten salt heat exchange system according to claim 1, characterized in that, Step S500 includes: Step S501: Identify the failure mechanism; Step S502: Define the damage parameters of the failure mechanisms and calculate the damage contribution of each failure mechanism; Step S503: Establish a damage accumulation model and perform multi-mechanism damage accumulation based on the damage contribution of each failure mechanism; Step S504: Predict the remaining lifespan of the system based on the current damage accumulation results.

6. The design and reliability analysis method for a large-capacity molten salt heat exchange system according to claim 1, characterized in that, Step S600 includes: Step S601: Identify the uncertainty parameters and determine their probability distribution; Step S602: Calculate the component reliability using a stress-intensity interference model and perform sensitivity analysis to identify key parameters among the uncertainty parameters; Step S603: Establish a system reliability model and perform a quantitative assessment of the system's reliability to obtain the system reliability. Step S604: Develop a risk-based detection strategy based on the system reliability and the component reliability.

7. The design and reliability analysis method for a large-capacity molten salt heat exchange system according to claim 1, characterized in that, Step S700 includes: Step S701: Based on the reliability quantitative assessment results, perform sensitivity analysis on key parameters to identify key variables; Step S702: Establish a multi-objective optimization model and perform optimization design to obtain an optimal solution; Step S703: Verify the optimization scheme through experiments or numerical simulations; Step S704: Iterate and improve the design based on the verification results until all design requirements are met.

8. A design and reliability analysis system for a large-capacity molten salt heat exchange system, characterized in that, include: The parameter and boundary determination unit is used to determine the system parameters and boundary conditions. The thermal-hydraulic calculation unit is used to perform thermal-hydraulic design calculations after the system parameters and boundary conditions are determined. The structural and thermal stress analysis unit is used for structural mechanics and thermal stress analysis. The performance evaluation and selection unit is used to evaluate and select material properties. The life prediction unit is used to predict the remaining life of the system based on the damage accumulation model. The reliability analysis unit is used to perform quantitative reliability assessment of the system based on probabilistic and statistical methods. The design optimization and verification unit is used to optimize and verify the design based on the results of the reliability quantitative assessment.

9. A storage medium, characterized in that, The storage medium stores a computer program adapted for loading by a processor to execute the steps of the design and reliability analysis method for a large-capacity molten salt heat exchange system as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the design and reliability analysis method for a large-capacity molten salt heat exchange system as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

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